Work

PCB-YOLOV8X: a network for detecting micro-sized defects on PCB surfaces based on enhanced feature information

Year: 2026

Type: article

Abstract: To address the challenges in PCB surface defect detection, including high labor costs, low detection efficiency, and insufficient detection accuracy, this study innovatively proposes a PCB surface micro-defect detection network called PCB-YOLOV8X based on the YOLOv8 model. By integrating the C2f-DCNV2 adaptive feature extraction module and the SPPF-LSKA efficient feature enhancement module, the network significantly improves the detection accuracy and efficiency for micro-sized defects. Furthermore, an optimized IWD-CIoU loss function is proposed to more precisely evaluate the matching degree between predicted and ground truth bounding boxes. In comparative experiments with different models, PCB-YOLOV8X achieves optimal performance on key metrics, including [email protected] of 98.44%, Precision of 97.67%, and Recall of 96.34%. Compared with the traditional YOLOv8 model, these represent improvements of 2.76% in [email protected], 2.33% in Precision, and 2.59% in Recall. The proposed PCB-YOLOv8X demonstrates enhanced capability in feature extraction and fusion in PCB surface micro-defect detection tasks, effectively solving issues such as low accuracy, missed detections, and false alarms in existing PCB defect detection models.

Source: Scientific Reports

DOI: 10.1038/s41598-026-46089-6

Authors: Xiaoyan Xu, Jennifer C. Dela Cruz, Ye Wang

Institutions: Mapúa University, Lishui University

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,690

Funders: National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province